<div align="center">
<img src="https://raw.githubusercontent.com/soccernet/soccernet/main/doc/images/soccernet.png">
</div>
[](https://img.shields.io/pypi/pyversions/SoccerNet)
[](https://pypi.org/project/SoccerNet/)
[](https://pepy.tech/project/SoccerNet)
[](https://pepy.tech/project/SoccerNet)
[](https://github.com/SoccerNet/SoccerNet/blob/master/LICENSE)
<!-- [](https://github.com/SoccerNet/SoccerNet/) -->
<!-- [](https://github.com/SoccerNet/SoccerNet/network) -->
<!-- [](https://github.com/SoccerNet/SoccerNet/issues) -->
<!-- [](http://www.repostatus.org/#active) -->
# SoccerNet package
```bash
conda create -n SoccerNet python pip
conda activate SoccerNet
pip install SoccerNet
# pip install -e https://github.com/SoccerNet/SoccerNet
# pip install -e .
```
## Structure of the data data for each game
- SoccerNet main folder
- Leagues (england_epl/europe_uefa-champions-league/france_ligue-1/...)
- Seasons (2014-2015/2015-2016/2016-2017)
- Games (format: "{Date} - {Time} - {HomeTeam} {Score} {AwayTeam}")
- SoccerNet-v2 - Labels / Manual Annotations
- **video.ini**: information on start/duration for each half of the game in the HQ video, in second
- **Labels-v2.json**: Labels from SoccerNet-v2 - action spotting
- **Labels-cameras.json**: Labels from SoccerNet-v1 - camera shot segmentation
- SoccerNet-v2 - Videos / Automatically Extracted Features
- **1_224p.mkv**: 224p video 1st half - timmed with start/duration from HQ video - resolution 224*398 - 25 fps
- **2_224p.mkv**: 224p video 2nd half - timmed with start/duration from HQ video - resolution 224*398 - 25 fps
- **1_720p.mkv**: 720p video 1st half - timmed with start/duration from HQ video - resolution 720*1280 - 25 fps
- **2_720p.mkv**: 720p video 2nd half - timmed with start/duration from HQ video - resolution 720*1280 - 25 fps
- **1_ResNET_TF2.npy**: ResNET features @2fps for 1st half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)
- **2_ResNET_TF2.npy**: ResNET features @2fps for 2nd half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)
- **1_ResNET_TF2_PCA512.npy**: ResNET features @2fps for 1st half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit), with dimensionality reduced to 512 using PCA
- **2_ResNET_TF2_PCA512.npy**: ResNET features @2fps for 2nd half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit), with dimensionality reduced to 512 using PCA
- **1_ResNET_5fps_TF2.npy**: ResNET features @5fps for 1st half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)
- **2_ResNET_5fps_TF2.npy**: ResNET features @5fps for 2nd half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)
- **1_ResNET_5fps_TF2_PCA512.npy**: ResNET features @5fps for 1st half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit), with dimensionality reduced to 512 using PCA
- **2_ResNET_5fps_TF2_PCA512.npy**: ResNET features @5fps for 2nd half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit), with dimensionality reduced to 512 using PCA
- **1_ResNET_25fps_TF2.npy**: ResNET features @25fps for 1st half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)
- **2_ResNET_25fps_TF2.npy**: ResNET features @25fps for 2nd half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)
- **1_player_boundingbox_maskrcnn.json**: Player Bounding Boxes @2fps for 1st half, extracted with MaskRCNN
- **2_player_boundingbox_maskrcnn.json**: Player Bounding Boxes @2fps for 2nd half, extracted with MaskRCNN
- **1_field_calib_ccbv.json**: Field Camera Calibration @2fps for 1st half, extracted with CCBV
- **2_field_calib_ccbv.json**: Field Camera Calibration @2fps for 2nd half, extracted with CCBV
- **1_baidu_soccer_embeddings.npy**: Frame Embeddings for 1st half from [https://github.com/baidu-research/vidpress-sports](https://github.com/baidu-research/vidpress-sports)
- **2_baidu_soccer_embeddings.npy**: Frame Embeddings for 2nd half from [https://github.com/baidu-research/vidpress-sports](https://github.com/baidu-research/vidpress-sports)
- Legacy from SoccerNet-v1
- **Labels.json**: Labels from SoccerNet-v1 - action spotting for goals/cards/subs only
- **1_C3D.npy**: C3D features @2fps for 1st half from SoccerNet-v1
- **2_C3D.npy**: C3D features @2fps for 2nd half from SoccerNet-v1
- **1_C3D_PCA512.npy**: C3D features @2fps for 1st half from SoccerNet-v1, with dimensionality reduced to 512 using PCA
- **2_C3D_PCA512.npy**: C3D features @2fps for 2nd half from SoccerNet-v1, with dimensionality reduced to 512 using PCA
- **1_I3D.npy**: I3D features @2fps for 1st half from SoccerNet-v1
- **2_I3D.npy**: I3D features @2fps for 2nd half from SoccerNet-v1
- **1_I3D_PCA512.npy**: I3D features @2fps for 1st half from SoccerNet-v1, with dimensionality reduced to 512 using PCA
- **2_I3D_PCA512.npy**: I3D features @2fps for 2nd half from SoccerNet-v1, with dimensionality reduced to 512 using PCA
- **1_ResNET.npy**: ResNET features @2fps for 1st half from SoccerNet-v1
- **2_ResNET.npy**: ResNET features @2fps for 2nd half from SoccerNet-v1
- **1_ResNET_PCA512.npy**: ResNET features @2fps for 1st half from SoccerNet-v1, with dimensionality reduced to 512 using PCA
- **2_ResNET_PCA512.npy**: ResNET features @2fps for 2nd half from SoccerNet-v1, with dimensionality reduced to 512 using PCA
## How to Download Games (Python)
```python
from SoccerNet.Downloader import SoccerNetDownloader
mySoccerNetDownloader = SoccerNetDownloader(LocalDirectory="path/to/soccernet")
# Download SoccerNet labels
mySoccerNetDownloader.downloadGames(files=["Labels.json"], split=["train", "valid", "test"]) # download labels
mySoccerNetDownloader.downloadGames(files=["Labels-v2.json"], split=["train", "valid", "test"]) # download labels SN v2
mySoccerNetDownloader.downloadGames(files=["Labels-cameras.json"], split=["train", "valid", "test"]) # download labels for camera shot
# Download SoccerNet features
mySoccerNetDownloader.downloadGames(files=["1_ResNET_TF2.npy", "2_ResNET_TF2.npy"], split=["train", "valid", "test"]) # download Features
mySoccerNetDownloader.downloadGames(files=["1_ResNET_TF2_PCA512.npy", "2_ResNET_TF2_PCA512.npy"], split=["train", "valid", "test"]) # download Features reduced with PCA
mySoccerNetDownloader.downloadGames(files=["1_player_boundingbox_maskrcnn.json", "2_player_boundingbox_maskrcnn.json"], split=["train", "valid", "test"]) # download Player Bounding Boxes inferred with MaskRCNN
mySoccerNetDownloader.downloadGames(files=["1_field_calib_ccbv.json", "2_field_calib_ccbv.json"], split=["train", "valid", "test"]) # download Field Calibration inferred with CCBV
mySoccerNetDownloader.downloadGames(files=["1_baidu_soccer_embeddings.npy", "2_baidu_soccer_embeddings.npy"], split=["train", "valid", "test"]) # download Frame Embeddings from https://github.com/baidu-research/vidpress-sports
# Download SoccerNet Challenge set (require password from NDA to download videos)
mySoccerNetDownloader.downloadGames(files=["1_ResNET_TF2.npy", "2_ResNET_TF2.npy"], split=["challenge"]) # download ResNET Features
mySoccerNetDownloader.downloadGames(files=["1_ResNET_TF2_PCA512.npy", "2_ResNET_TF2_PCA512.npy"], split=["challenge"]) # download ResNET Features reduced with PCA
mySoccerNetDownloader.downloadGames(files=["1_224p.mkv", "2_224p.mkv"], split=["challenge"]) # download 224p Videos (require password from NDA)
mySoccerNetDownloader.downloadGames(files=["1_720p.mkv", "2_720p.mkv"], split=["challenge"]) # download 720p Videos (require password from NDA)
mySoccerNetDownloader.downloadGames(files=["1_player_boundingbox_maskrcnn.json", "2_player_boundingbox_maskrcnn.json"], split=["challenge"]) # download Player Bounding Boxes inferred with MaskRCNN
mySoccerNetDownloader.downloadGames(files=["1_field_calib_ccbv.json", "2_field_calib_ccbv.json"], split=["challenge"]) # download Field Calibration inferred with CCBV
mySoccerNetDownloader.downloadGames(files=["1_baidu_soccer_embeddings.npy", "2_baidu_soccer_embeddings.npy"], split=["challenge"]) # download Frame Embeddings from https://github.com/baidu-research/vidpress-sports
# Download development kit per task
mySoccerNetDownloader.downloadDataTask(task="calibration-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="caption-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="jersey-2023", split=["train", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="reid-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="spotting-2023", split=["train", "valid", "test", "challenge"])
mySoccerNetDownloader.downloadDataTask(task="spotting-ball-2023", split=["train", "valid", "test", "challenge"], password=<PW_FROM_NDA>)
mySoccerNetDownloader.downloadDataTask(task="tracking-2023", split=["train", "test", "challenge"])
# Download SoccerNet videos (require password from NDA to download videos)
mySoccerNetDownloader.password = input("Password for videos? (contact the author):\n")
mySoccerNetDownloader.downloadGames(files=["1_224p.mkv", "2_224p.mkv"], split=["train", "valid", "test"]) # download 224p Videos
mySoccerNetDownloader.downloadGames(files=["1_720p.mkv", "2_720p.mkv"], split=["train", "valid", "test"]) # download 720p Videos
mySoccerNetDownloader.downloadRAWVideo(dataset="SoccerNet") # download 720p Videos
mySoccerNetDownloader.downloadRAWVideo(dataset="SoccerNet-Tracking") # download single camera RAW Videos
```
## How to read the list Games (Python)
```python
from SoccerNet.utils import getListGames
print(getListGames(split="train")) # return list of games recommended for training
print(getListGames(split="valid")) # return list of games recommended for validation
print(getListGames(split="test")) # return list of games recommended for testing
print(getListGames(split="challenge")) # return list of games recommended for challenge
print(getListGames(split=["train", "valid", "test", "challenge"])) # return list of games for training, validation and testing
print(getListGames(split="v1")) # return list of games from SoccerNetv1 (train/valid/test)
```
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"description": "<div align=\"center\">\n <img src=\"https://raw.githubusercontent.com/soccernet/soccernet/main/doc/images/soccernet.png\">\n</div>\n\n[](https://img.shields.io/pypi/pyversions/SoccerNet)\n[](https://pypi.org/project/SoccerNet/)\n[](https://pepy.tech/project/SoccerNet)\n[](https://pepy.tech/project/SoccerNet)\n[](https://github.com/SoccerNet/SoccerNet/blob/master/LICENSE)\n<!-- [](https://github.com/SoccerNet/SoccerNet/) -->\n<!-- [](https://github.com/SoccerNet/SoccerNet/network) -->\n<!-- [](https://github.com/SoccerNet/SoccerNet/issues) -->\n<!-- [](http://www.repostatus.org/#active) -->\n\n# SoccerNet package\n\n```bash\nconda create -n SoccerNet python pip\nconda activate SoccerNet\npip install SoccerNet\n# pip install -e https://github.com/SoccerNet/SoccerNet\n# pip install -e .\n```\n\n## Structure of the data data for each game\n\n- SoccerNet main folder\n - Leagues (england_epl/europe_uefa-champions-league/france_ligue-1/...)\n - Seasons (2014-2015/2015-2016/2016-2017)\n - Games (format: \"{Date} - {Time} - {HomeTeam} {Score} {AwayTeam}\")\n - SoccerNet-v2 - Labels / Manual Annotations\n - **video.ini**: information on start/duration for each half of the game in the HQ video, in second\n - **Labels-v2.json**: Labels from SoccerNet-v2 - action spotting\n - **Labels-cameras.json**: Labels from SoccerNet-v1 - camera shot segmentation\n\n - SoccerNet-v2 - Videos / Automatically Extracted Features\n - **1_224p.mkv**: 224p video 1st half - timmed with start/duration from HQ video - resolution 224*398 - 25 fps\n - **2_224p.mkv**: 224p video 2nd half - timmed with start/duration from HQ video - resolution 224*398 - 25 fps\n - **1_720p.mkv**: 720p video 1st half - timmed with start/duration from HQ video - resolution 720*1280 - 25 fps\n - **2_720p.mkv**: 720p video 2nd half - timmed with start/duration from HQ video - resolution 720*1280 - 25 fps\n - **1_ResNET_TF2.npy**: ResNET features @2fps for 1st half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)\n - **2_ResNET_TF2.npy**: ResNET features @2fps for 2nd half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)\n - **1_ResNET_TF2_PCA512.npy**: ResNET features @2fps for 1st half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit), with dimensionality reduced to 512 using PCA\n - **2_ResNET_TF2_PCA512.npy**: ResNET features @2fps for 2nd half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit), with dimensionality reduced to 512 using PCA\n - **1_ResNET_5fps_TF2.npy**: ResNET features @5fps for 1st half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)\n - **2_ResNET_5fps_TF2.npy**: ResNET features @5fps for 2nd half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)\n - **1_ResNET_5fps_TF2_PCA512.npy**: ResNET features @5fps for 1st half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit), with dimensionality reduced to 512 using PCA\n - **2_ResNET_5fps_TF2_PCA512.npy**: ResNET features @5fps for 2nd half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit), with dimensionality reduced to 512 using PCA\n - **1_ResNET_25fps_TF2.npy**: ResNET features @25fps for 1st half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)\n - **2_ResNET_25fps_TF2.npy**: ResNET features @25fps for 2nd half from SoccerNet-v2, [extracted using TF2](https://github.com/SilvioGiancola/SoccerNetv2-DevKit)\n - **1_player_boundingbox_maskrcnn.json**: Player Bounding Boxes @2fps for 1st half, extracted with MaskRCNN\n - **2_player_boundingbox_maskrcnn.json**: Player Bounding Boxes @2fps for 2nd half, extracted with MaskRCNN\n - **1_field_calib_ccbv.json**: Field Camera Calibration @2fps for 1st half, extracted with CCBV\n - **2_field_calib_ccbv.json**: Field Camera Calibration @2fps for 2nd half, extracted with CCBV\n - **1_baidu_soccer_embeddings.npy**: Frame Embeddings for 1st half from [https://github.com/baidu-research/vidpress-sports](https://github.com/baidu-research/vidpress-sports)\n - **2_baidu_soccer_embeddings.npy**: Frame Embeddings for 2nd half from [https://github.com/baidu-research/vidpress-sports](https://github.com/baidu-research/vidpress-sports)\n\n - Legacy from SoccerNet-v1\n - **Labels.json**: Labels from SoccerNet-v1 - action spotting for goals/cards/subs only\n - **1_C3D.npy**: C3D features @2fps for 1st half from SoccerNet-v1\n - **2_C3D.npy**: C3D features @2fps for 2nd half from SoccerNet-v1\n - **1_C3D_PCA512.npy**: C3D features @2fps for 1st half from SoccerNet-v1, with dimensionality reduced to 512 using PCA\n - **2_C3D_PCA512.npy**: C3D features @2fps for 2nd half from SoccerNet-v1, with dimensionality reduced to 512 using PCA\n - **1_I3D.npy**: I3D features @2fps for 1st half from SoccerNet-v1\n - **2_I3D.npy**: I3D features @2fps for 2nd half from SoccerNet-v1\n - **1_I3D_PCA512.npy**: I3D features @2fps for 1st half from SoccerNet-v1, with dimensionality reduced to 512 using PCA\n - **2_I3D_PCA512.npy**: I3D features @2fps for 2nd half from SoccerNet-v1, with dimensionality reduced to 512 using PCA\n - **1_ResNET.npy**: ResNET features @2fps for 1st half from SoccerNet-v1\n - **2_ResNET.npy**: ResNET features @2fps for 2nd half from SoccerNet-v1\n - **1_ResNET_PCA512.npy**: ResNET features @2fps for 1st half from SoccerNet-v1, with dimensionality reduced to 512 using PCA\n - **2_ResNET_PCA512.npy**: ResNET features @2fps for 2nd half from SoccerNet-v1, with dimensionality reduced to 512 using PCA\n\n\n## How to Download Games (Python)\n\n```python\nfrom SoccerNet.Downloader import SoccerNetDownloader\n\nmySoccerNetDownloader = SoccerNetDownloader(LocalDirectory=\"path/to/soccernet\")\n\n# Download SoccerNet labels\nmySoccerNetDownloader.downloadGames(files=[\"Labels.json\"], split=[\"train\", \"valid\", \"test\"]) # download labels\nmySoccerNetDownloader.downloadGames(files=[\"Labels-v2.json\"], split=[\"train\", \"valid\", \"test\"]) # download labels SN v2\nmySoccerNetDownloader.downloadGames(files=[\"Labels-cameras.json\"], split=[\"train\", \"valid\", \"test\"]) # download labels for camera shot\n\n# Download SoccerNet features\nmySoccerNetDownloader.downloadGames(files=[\"1_ResNET_TF2.npy\", \"2_ResNET_TF2.npy\"], split=[\"train\", \"valid\", \"test\"]) # download Features\nmySoccerNetDownloader.downloadGames(files=[\"1_ResNET_TF2_PCA512.npy\", \"2_ResNET_TF2_PCA512.npy\"], split=[\"train\", \"valid\", \"test\"]) # download Features reduced with PCA\nmySoccerNetDownloader.downloadGames(files=[\"1_player_boundingbox_maskrcnn.json\", \"2_player_boundingbox_maskrcnn.json\"], split=[\"train\", \"valid\", \"test\"]) # download Player Bounding Boxes inferred with MaskRCNN\nmySoccerNetDownloader.downloadGames(files=[\"1_field_calib_ccbv.json\", \"2_field_calib_ccbv.json\"], split=[\"train\", \"valid\", \"test\"]) # download Field Calibration inferred with CCBV\nmySoccerNetDownloader.downloadGames(files=[\"1_baidu_soccer_embeddings.npy\", \"2_baidu_soccer_embeddings.npy\"], split=[\"train\", \"valid\", \"test\"]) # download Frame Embeddings from https://github.com/baidu-research/vidpress-sports\n\n# Download SoccerNet Challenge set (require password from NDA to download videos)\nmySoccerNetDownloader.downloadGames(files=[\"1_ResNET_TF2.npy\", \"2_ResNET_TF2.npy\"], split=[\"challenge\"]) # download ResNET Features\nmySoccerNetDownloader.downloadGames(files=[\"1_ResNET_TF2_PCA512.npy\", \"2_ResNET_TF2_PCA512.npy\"], split=[\"challenge\"]) # download ResNET Features reduced with PCA\nmySoccerNetDownloader.downloadGames(files=[\"1_224p.mkv\", \"2_224p.mkv\"], split=[\"challenge\"]) # download 224p Videos (require password from NDA)\nmySoccerNetDownloader.downloadGames(files=[\"1_720p.mkv\", \"2_720p.mkv\"], split=[\"challenge\"]) # download 720p Videos (require password from NDA)\nmySoccerNetDownloader.downloadGames(files=[\"1_player_boundingbox_maskrcnn.json\", \"2_player_boundingbox_maskrcnn.json\"], split=[\"challenge\"]) # download Player Bounding Boxes inferred with MaskRCNN \nmySoccerNetDownloader.downloadGames(files=[\"1_field_calib_ccbv.json\", \"2_field_calib_ccbv.json\"], split=[\"challenge\"]) # download Field Calibration inferred with CCBV \nmySoccerNetDownloader.downloadGames(files=[\"1_baidu_soccer_embeddings.npy\", \"2_baidu_soccer_embeddings.npy\"], split=[\"challenge\"]) # download Frame Embeddings from https://github.com/baidu-research/vidpress-sports\n\n# Download development kit per task\nmySoccerNetDownloader.downloadDataTask(task=\"calibration-2023\", split=[\"train\", \"valid\", \"test\", \"challenge\"])\nmySoccerNetDownloader.downloadDataTask(task=\"caption-2023\", split=[\"train\", \"valid\", \"test\", \"challenge\"])\nmySoccerNetDownloader.downloadDataTask(task=\"jersey-2023\", split=[\"train\", \"test\", \"challenge\"])\nmySoccerNetDownloader.downloadDataTask(task=\"reid-2023\", split=[\"train\", \"valid\", \"test\", \"challenge\"])\nmySoccerNetDownloader.downloadDataTask(task=\"spotting-2023\", split=[\"train\", \"valid\", \"test\", \"challenge\"])\nmySoccerNetDownloader.downloadDataTask(task=\"spotting-ball-2023\", split=[\"train\", \"valid\", \"test\", \"challenge\"], password=<PW_FROM_NDA>)\nmySoccerNetDownloader.downloadDataTask(task=\"tracking-2023\", split=[\"train\", \"test\", \"challenge\"])\n\n# Download SoccerNet videos (require password from NDA to download videos)\nmySoccerNetDownloader.password = input(\"Password for videos? (contact the author):\\n\")\nmySoccerNetDownloader.downloadGames(files=[\"1_224p.mkv\", \"2_224p.mkv\"], split=[\"train\", \"valid\", \"test\"]) # download 224p Videos\nmySoccerNetDownloader.downloadGames(files=[\"1_720p.mkv\", \"2_720p.mkv\"], split=[\"train\", \"valid\", \"test\"]) # download 720p Videos \nmySoccerNetDownloader.downloadRAWVideo(dataset=\"SoccerNet\") # download 720p Videos \nmySoccerNetDownloader.downloadRAWVideo(dataset=\"SoccerNet-Tracking\") # download single camera RAW Videos \n```\n\n## How to read the list Games (Python)\n\n```python\nfrom SoccerNet.utils import getListGames\nprint(getListGames(split=\"train\")) # return list of games recommended for training\nprint(getListGames(split=\"valid\")) # return list of games recommended for validation\nprint(getListGames(split=\"test\")) # return list of games recommended for testing\nprint(getListGames(split=\"challenge\")) # return list of games recommended for challenge\nprint(getListGames(split=[\"train\", \"valid\", \"test\", \"challenge\"])) # return list of games for training, validation and testing\nprint(getListGames(split=\"v1\")) # return list of games from SoccerNetv1 (train/valid/test)\n```\n\n\n",
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